An Effective Method for Eye Detection Based on Texture Information

Chiu, Chi-Huang; Wu, Ruey-Shyang; Tut, Chi-Io; Lin, Hesheng; Yuan, Shyan-Ming

doi:10.1109/iccit.2007.288

Cited by 17 publications

(3 citation statements)

References 9 publications

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“…Firstly, the contrast is enhanced by homomorphism filtering [7] . Then, we construct a circle filter to stand out the circle character, and the noise is removed by mean filtering [8] , subsequently. Lastly, the image is enhanced by a Gabor filter [9] for its direction and frequency selectivity.…”

Section: Eye Location Based On Region Feature Image Preprocessingmentioning

confidence: 99%

Eye Location Based on Adaboost and Region Features

Zhang

Tang

Jin

et al. 2011

AMM

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In this paper, we proposed a novel eye location method based on Adaboost and region features. Firstly, Haar features and Adaboost algorithm are used to extract the eye regions from a face image. Then, we highlight the characteristics of eyes to eye location. The method proposed have been tested in the CAS-PEAL-R1 database and CASIA NIR database separately, and the accuracy rate is 98.86% and 97.68%, which demonstrates the effectiveness of the method

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Section: Eye Location Based On Region Feature Image Preprocessingmentioning

confidence: 99%

Eye Location Based on Adaboost and Region Features

Zhang

Tang

Jin

et al. 2011

AMM

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“…• Template-based methods that slide a pre-designed eye model (template) across a face image to obtain the best eye matches. Park et al [2] determined the eye candidates by using texturebased eye filtering, and then detected the eye locations using 978-1-4577-1019-3/11/$26.00 c 2011 IEEE. face geometry.…”

Section: Introductionmentioning

confidence: 99%

Pupil detection under lighting and pose variations in the visible and active infrared bands

Bourlai

Whitelam

Kakadiaris

2011

2011 IEEE International Workshop on Information Forensics and Security

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We propose a novel and efficient methodology for the detection of human pupils using face images acquired under controlled and difficult (large pose and illumination changes) conditions in variable spectra (i.e., visible, multi-spectral, and short wave infrared (SWIR)). The methodology is based on template matching, and is composed of an offline and an online mode. During the offline mode, band-dependent eye templates are generated for each eye from the face images of a pre-selected number of subjects. Using the eye templates that are generated in the offline mode, the online pupil detection mode determines the locations of the human eyes and the pupils. A combination of texture-and template-based matching algorithms is used for this purpose. Our method achieved a significantly high detection rate, yielding an average of 96.38% pupil detection accuracy across all datasets used. Based on a comparative analysis on different databases, we concluded that: (i) a single methodological approach can be used to efficiently detect human eyes and pupils of face images (with strong pose and illumination variations) acquired in the visible and hyper-spectral bands, and (ii) the use of texture-based matching and normalized band-specific templates significantly increases detection accuracy. To the best of our knowledge, this is the first time in the open literature that the problem of multi-band pupil detection on face images in the presence of lighting and pose variations, is being investigated using a unified algorithm.

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“…Although these methods are typically efficient, they are not as accurate as other eye detection methods (appearance-and templatebased methods), especially for low-contrast images. -Appearance-Based methods [12,[16][17][18][19]: in such methods, eye detection is considered the problem of classifying each scanned subwindow as one of two classes (i.e., eye and non-eye). Appearance-based methods avoid difficulties in modeling 3D (depth) structure of the human eye by focusing on possible eye appearances under various conditions.…”

Section: Introductionmentioning

confidence: 99%

On designing an unconstrained tri-band pupil detection system for human identification

Whitelam

Bourlai

2015

Machine Vision and Applications

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The majority of facial recognition systems depend on the accurate location of both the left and right eye centers in an effort to geometrically normalize the face images available in a database under study. In this paper, we propose a novel pupil detection algorithmic approach that automatically and efficiently locates the eye centers of face images captured using visible and infrared sensors when operating under challenging conditions. Our proposed approach includes the usage of long-range and night-time face images captured in the infrared (IR) band under active illumination. It also efficiently deals with partial face obstruction (subjects wearing eye glasses) as well as face pose and illumination variation. Our scenario-adaptable methodological approach involves a number of algorithmic steps, including (i) situation classification (to automatically determine the acquisition scenario each input face image is coming from), (ii) generation and usage of 2D normalized correlation coefficients, (iii) prediction of eye localization, (iv) computation of summation range filters for accurate pupil detection, and an (v) eye glasses classifier on facial images using support vector machines (to determine when subjects are wearing glasses or not). Our proposed approach is compared against state-of-the-art academic and commercial eye detection algorithms, including the (a) Viola and Jones Adaboost method, (b) one of the latest academic eye detection algorithms proposed by Valenti and Gevers (IEEE TPAMI), and (c) the eye detection approach that is available as part of the G8 commercial face recognition software B Cameron Whitelam (package provided by L1 systems). Experimental results demonstrate that our proposed approach outperforms all other approaches when applied on various challenging face datasets, including IR face images captured behind tinted glass or at long ranges up to about 350 feet away, day or night. We also show the benefits of our approach to face normalization and, as a result, face recognition improvement performance in terms of rank-1 identification rates. This is an important achievement that has practical value for forensic tool operators who may have to manually localize the eye centers of all face images available in a dataset, before further face image preprocessing and face-based matching algorithms can be applied.

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An Effective Method for Eye Detection Based on Texture Information

Cited by 17 publications

References 9 publications

Eye Location Based on Adaboost and Region Features

Eye Location Based on Adaboost and Region Features

Pupil detection under lighting and pose variations in the visible and active infrared bands

On designing an unconstrained tri-band pupil detection system for human identification

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